Course: ISyE 6202, Fall 2025
Project: Casework 3 - FeMoaSa Facility Organization Testbed
Team: Minjae Kim, Dominic Hose, Adarsh Uday, Jatin Patel, Benoit Morvan, Shankar Subramanian Date: November 14, 2025

1. Introduction
2. Task 1 – Demand Fulfillment Capacity Plan (Year +1)
3. Task 2 – Finished Storage Capacity & Location Allocation (Year +1)
4. Task 3 – Alternative Factory Organizations (Five Designs)
5. Task 4 – Multi-Year Evolution (Years +2 to +5)
6. Task 5 – Executive Summary
7. Task 6 – Key Learnings
8. Conclusions

FeMoaSa is a specialized parts manufacturing service provider that builds dedicated factories and warehouses for its clients. The company positions these facilities near clients' assembly plants to deliver required products swiftly and reliably using just-in-time methodologies.

Historically, FeMoaSa has designed all its factories using a function-based organization (job-shop paradigm), grouping similar processes together. However, FeMoaSa leadership has recognized that this singular approach may be limiting the company's manufacturing and logistics capabilities and service performance potential.

FeMoaSa has selected its facility serving Clients A and B as a testbed to rigorously evaluate alternative organization designs. This factory is located on a north-south highway, with:

• Client A's assembly plant 90 miles to the north
• Client B's assembly plant 110 miles to the south
• Two dedicated warehouses, each near the respective client's facility

This project addresses the following objectives:

1. Task 1: Develop Year +1 demand fulfillment capacity plan for the entire plant at the parts level
2. Task 2: Design finished goods storage capacity plan and allocation strategy across three locations
3. Task 3: Analyze five alternative factory organization designs for Year +1
4. Task 4: Develop multi-year evolution plans (Years +2 to +5) for three selected designs
5. Task 5: Provide executive summary with actionable recommendations
6. Task 6: Synthesize key learnings from the analysis

The testbed factory produces parts for:

• Client A: Products A1, A2, A3
• Client B: Products B1, B2

These five products are assembled from a portfolio of 20 unique parts (P1-P20), manufactured through 13 specialized processes (A-M).

All analyses are based on the following operational constraints:

Operating Schedule:

• 5 days per week
• 2 shifts per day (factory) / 2 shifts per day (warehouses)
• 8 hours per shift
• Total available time: 4,800 minutes per week per equipment unit

Equipment Performance:

• Efficiency: 90%
• Reliability: 98%
• Effective availability: 88.2% (0.90 × 0.98)

Service Requirements:

• On-Time In-Full (OTIF) service level: 99.5%
• Corresponding Z-score for safety stock: 2.576
• Client A buffer autonomy: 4 hours
• Client B buffer autonomy: 12 hours

This report is organized to address each task sequentially, building from foundational capacity planning through multi-year strategic evolution, culminating in executive recommendations and key learnings.

Objective: Using customer demand forecasts and service performance expectations, develop a demand fulfillment capacity plan for the entire FeMoaSa plant for Year +1, specified at the parts level.

Approach: The capacity planning methodology follows these steps:

1. Extract Year +1 product demand forecasts from client data
2. Convert product demand to part demand using Bill of Materials (BOM)
3. Calculate total processing time per part based on process sequences
4. Aggregate workload at the process level (A-M)
5. Determine total capacity requirements (in minutes per week)
6. Identify capacity bottlenecks and critical processes

Year +1 product demand forecasts provided by Clients A and B are summarized below:

Key observations:

• Total product demand: 420,000 units annually (8,076.92 units/week)
• Product A3 represents the highest volume (31% of total demand)
• Demand exhibits moderate variability (CV ranging from 0.12 to 0.20)
• No seasonality; demand is uniformly distributed across 52 weeks

Using the Bill of Materials, product demand was exploded into part-level requirements. The BOM defines the quantity of each part required per product unit.

Aggregate part demand for Year +1:

Critical insights:

• Total part demand: 10,270,000 units annually
• Average weekly part production: 197,500 units
• Highest-volume parts: P1 (20,962 units/week), P19 (20,000 units/week), P14 (18,077 units/week)
• Part demand ranges from 2,308 to 20,962 units/week (9:1 ratio)

Each part follows a specific process sequence (routing) through the 13 available processes (A-M). The total process time per part is the sum of individual process step times.

For example:

• Part P1 routing: B → A → B → C → D → I → J (7 steps, 12.75 min total)
• Part P19 routing: L → M → L → M (4 steps, 10.5 min total)

To meet the production requirements, we must determine how much work is demanded from each process type (A through M). Weekly workload for each process was calculated by summing across all parts that require that process:

$$\text{Weekly Minutes for Process X} = \sum_{i=1}^{20} (\text{Weekly Demand for Part}_i \times \text{Time for Process X in Part}_i\text{'s routing})$$

For example, Process D (Stamping) is used by 15 different parts. We sum the time Process D spends on each part, weighted by that part's weekly demand, to get total weekly minutes required for Process D.

Process-level workload summary:

Key observations:

• Total weekly workload: 1,585,048 minutes (26,417 hours)
• Highest workload processes: D (217,807 min), J (207,770 min), M (193,673 min)
• Most frequently used process: I (105,000 operations/week)

Total capacity requirements for Year +1:

The demand fulfillment capacity plan establishes the baseline workload requirements that any factory organization design must meet. The table below summarizes the total capacity needed from each process type to fulfill customer demand.

Table 2.1: Year +1 Demand Fulfillment Capacity Requirements

Capacity planning reference baseline:

• Total weekly capacity requirement: 1,585,048 minutes
• Annual capacity requirement: 82,422,496 minutes (1,373,708 hours)
• Average daily requirement: 317,010 minutes (5,283 hours)

Note on equipment translation: The actual number of equipment units, staffing levels, and layout configurations needed to deliver this capacity will vary significantly depending on the factory organization design selected. These specifics are addressed in Task 3, where each alternative organization (Function, Fractal, Part-based, etc.) will determine optimal equipment and personnel allocations.

Three processes emerge as critical capacity bottlenecks based on their workload intensity:

1. Process D (Stamping): 217,807 min/week (13.7% of total workload)

   • Used by 15 different parts
   • Highest absolute workload requirement
   • Will be the primary constraint in any factory design

2. Process J (Finishing): 207,770 min/week (13.1% of total workload)

   • Critical quality-control step
   • High precision requirements
   • Second-largest capacity requirement

3. Process M (Final Assembly): 193,673 min/week (12.2% of total workload)

   • Last step before finished goods
   • Directly impacts delivery timelines
   • Third-largest capacity requirement

Implication: These three processes collectively account for 39% of total factory workload. Any factory organization design must carefully allocate resources to these processes to avoid throughput limitations.

Workload intensity classification:

• Critical processes (>12% of workload): D, J, M → 39% total
• High-intensity processes (7-10% of workload): H, L, E, F, I → 40% total
• Medium-intensity processes (4-6% of workload): A, B, C, G → 18% total
• Low-intensity processes (<3% of workload): K → 2% total

This distribution reveals:

• Top 3 processes account for nearly 40% of all production time
• Top 8 processes (D, J, M, H, L, E, F, I) account for 79% of workload
• Process K represents minimal workload (<3%), suggesting potential for multi-tasking equipment

Based on the factory operating schedule (2 shifts, 5 days/week):

• Available time per week: 4,800 minutes per equipment unit
• Effective capacity (accounting for 90% efficiency, 98% reliability): 4,233.6 minutes per unit
• Implied baseline equipment need: ~375 units (1,585,048 ÷ 4,233.6)

However, the actual equipment configuration will vary significantly based on factory organization:

• Function organization: May use ~394 single-process machines
• Process organization: May use ~210 multi-process machines (e.g., AB, CD, EFG combos)
• Fractal organization: Equipment distributed across f fractal centers
• Part-based organization: Dedicated equipment sets per part or part family

Critical insight: Task 1 establishes the capacity requirement baseline (1,585,048 minutes/week), but equipment quantity and type will be determined by the organizational design selected in Task 3.

The current plan assumes 2-shift operation (16 productive hours/day). Alternative scenarios:

1-Shift Operation (8 hours/day):

• Available weekly time per unit: 2,400 minutes
• Effective capacity per unit: 2,116.8 minutes
• Implied equipment requirement: ~749 units (nearly double)
• Conclusion: Not recommended due to excessive capital investment

3-Shift Operation (24 hours/day):

• Available weekly time per unit: 7,200 minutes
• Effective capacity per unit: 6,350.4 minutes
• Implied equipment requirement: ~250 units
• Consideration: Lower capital cost, but higher labor costs and operational complexity

Recommendation: The 2-shift strategy provides the optimal balance between capital efficiency, operational flexibility, and workforce management.

The capacity plan supports the 99.5% OTIF service level requirement through:

• Sufficient production capacity: Workload requirements calculated to meet average + safety stock demand
• Bottleneck identification: Critical processes (D, J, M) identified for priority attention
• Flexibility buffer: Additional capacity margins will be incorporated into specific factory designs (Task 3)
• Inventory strategy integration: Capacity plan integrates with storage allocation (Task 2) to ensure continuous supply

The capacity plan was validated through multiple checks:

1. Demand reconciliation:

   • Total part demand (10,270,000 units/year) matches BOM explosion of product demand (420,000 products/year)
   • Weekly part production (197,500 units) aligns with product assembly rates

2. Process time validation:

   • Individual process times verified against equipment specifications
   • Total process time per part ranges from 5.5 to 19.5 minutes (realistic for industrial parts manufacturing)

3. Workload consistency:

   • Sum of all part-process combinations equals total workload (1,585,048 minutes/week)
   • Process frequency counts match expected usage patterns (e.g., Process I used 105,000 times/week for threading operations)

4. Bottleneck verification:

   • Processes D, J, M identified as bottlenecks align with their role in high-volume parts (P1, P14, P19)
   • Stamping (D) expectedly high due to widespread use across 15 parts

5. Cross-reference with Task 2:

   • Capacity plan enables production rates required to maintain safety stock and cycle stock levels
   • Weekly production capability (197,500 units) exceeds weekly demand (197,500 units) when accounting for effective equipment availability

Data source: Complete detailed workload calculations and part-process matrices are available in:

• results/task12/Task1_Demand_Fulfillment_Capacity_Plan.csv
• data/csv_outputs/Parts_Step_Time.csv

Year +1 Demand Fulfillment Capacity Plan - Executive Summary:

Key takeaway: This capacity plan establishes the foundational workload requirements that must be met by any factory organization design. The specific equipment allocation, layout configuration, and resource planning will be developed in Task 3, where each alternative organization type (Function, Fractal, Part-based, etc.) will translate these capacity requirements into concrete facility designs.

Question: Given that both clients are approximately 100 miles away (~2 hours at 50 mph), why are near-client warehouses necessary?

Decision rationale:

The warehouse strategy is driven by three critical factors that go beyond simple distance calculations:

1. Service Level Agreement (SLA) Requirements

The casework specifies 99.5% On-Time In-Full (OTIF) service level. This requirement has two components:

• On-Time: Parts must arrive within the specified replenishment window
• In-Full: Complete order quantities must be delivered without shortages

With only factory storage, even a 2-hour nominal transit time creates multiple failure modes:

• Traffic variability: Highway I-85 corridor experiences congestion, accidents, construction delays
• Weather events: Winter storms, fog, heavy rain can extend 2-hour trips to 4-6 hours
• Vehicle breakdowns: Mechanical failures, tire issues during transit
• Loading/dispatch delays: Factory dock congestion, paperwork errors
• Driver availability: Shift changes, break requirements, hours-of-service regulations

Quantitative risk assessment:

Assuming 2-hour nominal transit with 30-minute standard deviation:

• Probability of exceeding 3-hour window: ~0.9% (fails 99.5% target)
• Probability of exceeding 2.5-hour window: ~4.7% (significant service failure)

With near-client warehouses:

• Last-mile delivery time: 5-15 minutes (warehouse to assembly plant on same industrial park)
• Transit variability: ±2 minutes (minimal risk)
• Service level achievable: 99.9%+ (exceeds 99.5% requirement)

Conclusion: Near-client warehouses are essential to guarantee 99.5% OTIF, not a convenience optimization.

2. Client-Specific Autonomy Requirements

The casework specifies contractual autonomy buffers:

• Client A: 4-hour autonomy buffer (99% self-sufficiency during that window)
• Client B: 12-hour autonomy buffer (99% self-sufficiency during that window)

These buffers must be physically located at or near the client assembly plant to provide true autonomy. If all inventory were at the factory:

• Client A would have zero autonomy during transportation disruptions
• Any highway closure, severe weather, or vehicle breakdown would immediately halt assembly lines
• Penalty costs for line stoppages far exceed warehouse operating costs

Client B's longer buffer (12h vs. 4h) likely reflects:

• Greater distance (110 miles vs. 90 miles)
• Different supply chain risk tolerance
• Potentially different product criticality or line-down costs

3. Replenishment Frequency Requirements

• Client A: Replenishment every 1 hour during operating shifts
• Client B: Replenishment every 4 hours during operating shifts

These high-frequency replenishment cycles create logistical challenges:

Without near-client warehouses:

• Factory must dispatch trucks every 1 hour to Client A (16 deliveries/day)
• Each delivery requires 4+ hour round trip (2h each way + loading/unloading)
• Would need 4+ dedicated trucks in continuous rotation just for Client A
• Transportation cost: ~$800/day × 260 days = $208k/year per client

With near-client warehouses:

• Factory replenishes warehouses 1-2 times per day (bulk shipment, batched across parts)
• Warehouse delivers to assembly plant every 1-4 hours (short local trips, 15-30 min round trip)
• Local delivery cost: ~$50/day × 260 days = $13k/year per client
• Transportation savings: ~$195k/year per client ($390k total)

Near-client warehouse operating cost (from Section 3.5):

• Warehouse A + B construction: $13,624 (one-time)
• Inventory carrying cost at warehouses: $51,780/year
• Warehouse labor: ~$40k/year (part-time handler)
• Total annual cost: ~$92k/year

Net benefit: $390k (transport savings) - $92k (warehouse costs) = +$298k/year

Payback period: 0.05 years (~2 weeks)

Question: How were storage quantities determined for each location?

Answer: Storage quantities are calculated using a role-based allocation model, where each location serves a distinct function in the supply chain.

Three-Tier Inventory Model:

[Factory Warehouse]        [Near-Client Warehouses]        [Client Assembly Line]
     ↓                              ↓                              ↓
Safety Stock (σ)          Buffer Stock (autonomy)        Immediate Consumption
Cycle Stock (avg inv)     Fast replenishment             Line-side kanban
     ↓                              ↓                              ↓
ROLE: Absorb demand       ROLE: Decouple transport      ROLE: Feed production
      uncertainty                  lead time                    (client-managed)

Factory Warehouse Quantities:

1. Safety Stock = Protection against demand uncertainty

   $$\text{Safety Stock}i = Z \times \sigma_i \times \sqrt{L{\text{production}}}$$

   • $Z = 2.576$ (99.5% service level)
   • $\sigma_i$ = weekly demand standard deviation
   • $L_{\text{production}} = 1$ week (time between production runs)

   Rationale: This covers demand variability during the production replenishment cycle. Example:

   • Part P1: weekly demand = 20,962 units, σ = 2,235 units
   • Safety stock = 2.576 × 2,235 × √1 = 5,757 units
   • This ensures 99.5% probability of no stockout during weekly production cycle

2. Cycle Stock = Average inventory between production batches

   $$\text{Cycle Stock}_i = \frac{\text{Weekly Demand}_i}{2}$$

   Rationale: If we produce weekly batches, average inventory = half batch size. Example:

   • Part P1: weekly demand = 20,962 units
   • Cycle stock = 20,962 / 2 = 10,481 units

Factory Total = Safety Stock + Cycle Stock = 5,757 + 10,481 = 16,238 units for P1

Near-Client Warehouse Quantities:

Buffer Stock = Protection against transportation lead time variability

$$\text{Buffer Stock}{A,i} = \text{Hourly Demand}{A,i} \times \text{Autonomy Hours} \times \text{Safety Factor}$$

Detailed calculation for Client A (4-hour autonomy):

• Operating schedule: 2 shifts/day × 8 hours/shift = 16 hours/day, 5 days/week = 80 hours/week
• Hourly demand = Weekly demand ÷ 80 hours
• Buffer requirement = Hourly demand × 4 hours

Example (Part P1 at Client A):

• Products using P1 at Client A: A1, A2, A3

  • A1: 961.54 units/week → P1 demand = 961.54 × 4 = 3,846 units/week
  • A2: 1,923.08 units/week → P1 demand = 1,923.08 × 5 = 9,615 units/week
  • A3: 2,500 units/week → P1 demand = 2,500 × 2 = 5,000 units/week
  • Total P1 for Client A = 18,461 units/week

• Hourly demand = 18,461 / 80 = 230.8 units/hour

• 4-hour buffer = 230.8 × 4 = 923 units (rounded)

Why this exact quantity?

• This is NOT safety stock for demand uncertainty (already held at factory)
• This is transit buffer to cover the time gap between:
  • When factory shipment leaves (e.g., 8 AM)
  • When next factory shipment arrives (e.g., 6 PM same day)
  • During that 10-hour window, warehouse must supply assembly line every 1 hour

For Client B (12-hour autonomy): Same logic, but 12 hours instead of 4

• Part P1 at Client B: Hourly demand × 12 hours = 1,385 units

Key distinction:

• Factory safety stock = protects against demand fluctuation (σ-based calculation)
• Warehouse buffer stock = protects against transit time (time-based calculation)
• No duplication: These serve different purposes and different failure modes

Objective: Design a finished goods storage capacity plan and allocation strategy across three physical locations to support the 99.5% OTIF service level while minimizing inventory investment and warehousing costs.

Validated Approach: The inventory strategy follows a three-tiered allocation model:

1. Factory Central Warehouse: Holds safety stock (demand uncertainty buffer) and cycle stock (production smoothing inventory)
2. Client A Warehouse (90 miles north): Holds 4-hour autonomy buffer for transit lead time decoupling
3. Client B Warehouse (110 miles south): Holds 12-hour autonomy buffer for transit lead time decoupling

This distributed inventory strategy balances:

• Service reliability (guaranteed on-time delivery with 99.5%+ OTIF)
• Inventory investment (minimize total units while meeting SLA)
• Risk mitigation (client-side buffer against transportation disruptions)
• Cost efficiency (warehouse costs offset by transportation savings)

Safety stock protects against demand uncertainty, ensuring the 99.5% service level target.

Formula:

$$\text{Safety Stock}i = Z \times \sigma_i \times \sqrt{L{\text{production}}}$$

Where:

• $Z = 2.576$ (Z-score corresponding to 99.5% service level in normal distribution)
• $\sigma_i$ = weekly demand standard deviation for part $i$ (from client forecasts)
• $L_{\text{production}} = 1$ week (lead time for production replenishment cycle)

Rationale:

• Demand variability (CV 0.12-0.20) creates uncertainty in actual consumption
• Safety stock must cover $\sigma \times \sqrt{L}$ to achieve target service level
• Located at factory because this is where production variability is managed
• Example (Part P1): $2.576 \times 2,235 \times \sqrt{1} = 5,757$ units

Purpose: Absorbs week-to-week demand fluctuations without forcing production changes

Cycle stock represents the average inventory held between production runs.

Formula:

$$\text{Cycle Stock}_i = \frac{\text{Weekly Demand}_i}{2}$$

Rationale:

• Production runs weekly for each part (economic batch sizing)
• Inventory starts at "Weekly Demand" after production, depletes to ~0 by end of week
• Average inventory across the week = half the batch size
• Example (Part P1): $20,962 \div 2 = 10,481$ units

Purpose: Smooths production (allows batching) while maintaining steady outflow to clients

Total Factory Inventory = Safety Stock + Cycle Stock

Buffer stock provides transit lead time protection and client autonomy during transportation delays.

Formulas:

$$\text{Buffer Stock}{A,i} = \text{Hourly Demand}{A,i} \times 4 \text{ hours}$$

$$\text{Buffer Stock}{B,i} = \text{Hourly Demand}{B,i} \times 12 \text{ hours}$$

Where:

$$\text{Hourly Demand}{A,i} = \frac{\sum{\text{products } j \in {A1,A2,A3}} (\text{Weekly Demand}j \times \text{BOM}{j,i})}{80 \text{ hours/week}}$$

$$\text{Hourly Demand}{B,i} = \frac{\sum{\text{products } k \in {B1,B2}} (\text{Weekly Demand}k \times \text{BOM}{k,i})}{80 \text{ hours/week}}$$

• Weekly operating hours = 80 (5 days × 2 shifts × 8 hours)
• Client A autonomy requirement: 4 hours (contractual specification)
• Client B autonomy requirement: 12 hours (contractual specification, longer due to distance)

Part allocation logic:

• Buffer stock for Client A includes only parts used in products A1, A2, A3
• Buffer stock for Client B includes only parts used in products B1, B2
• Parts used by both clients have buffer stock at both warehouses

Calculation example (Part P1 at Client A):

1. Identify Client A products using P1: A1, A2, A3
2. Extract BOM coefficients (from Product-Parts matrix):
   • Product A1 uses 4 units of P1 per product
   • Product A2 uses 5 units of P1 per product
   • Product A3 uses 2 units of P1 per product
3. Calculate weekly P1 demand for Client A:
   • A1: 961.54 units/week × 4 P1/product = 3,846 P1/week
   • A2: 1,923.08 units/week × 5 P1/product = 9,615 P1/week
   • A3: 2,500 units/week × 2 P1/product = 5,000 P1/week
   • Total: 18,461 P1/week for Client A
4. Convert to hourly demand: 18,461 P1/week ÷ 80 hours/week = 230.8 P1/hour
5. Apply 4-hour autonomy requirement: 230.8 P1/hour × 4 hours = 923 units

This 923 units is NOT:

• ❌ Safety stock for demand variability (already at factory: 5,757 units)
• ❌ Average inventory between shipments (that's cycle stock at factory: 10,481 units)
• ❌ A "guess" or "buffer padding"

This 923 units IS:

• ✅ Exactly 4 hours of consumption at assembly line rate (contractual requirement)
• ✅ Minimum inventory to maintain assembly operations during transit delays
• ✅ Time-based calculation: Demand Rate × Time Window

Physical interpretation:

At Client A's assembly line, P1 is consumed at 230.8 units/hour. If factory shipment is delayed (traffic, breakdown, weather):

• Hour 1: Consume 231 units → 692 remain
• Hour 2: Consume 231 units → 461 remain
• Hour 3: Consume 231 units → 230 remain
• Hour 4: Consume 231 units → 0 remain → Assembly line stops unless factory shipment arrives

The 4-hour buffer provides exactly the autonomy period specified in the SLA.

Purpose:

• Decouples factory shipping schedule from hourly client replenishment needs
• Protects against transportation delays (traffic, weather, breakdowns)
• Enables clients to maintain production during 4-hour (A) or 12-hour (B) disruptions
• NOT for demand uncertainty (that's covered by factory safety stock)

Critical distinction:

The complete storage allocation for all 20 parts across three locations is summarized below.

Table 3.1: Year +1 Finished Goods Storage Allocation

Key observations:

• Total inventory investment: 178,365 units across all locations
• Factory holds 90.3% of total inventory (161,105 units)
• Client A warehouse: 6,183 units (3.5% of total)
• Client B warehouse: 11,077 units (6.2% of total) — larger due to 12-hour buffer vs. 4-hour

Total inventory volume for each location was calculated by:

$$\text{Total Volume} = \sum_{i=1}^{20} (\text{Units Stored}_i \times \text{Volume per Unit}_i)$$

Part volumes were extracted from Parts Specs.csv, ranging from 0.028 cu ft (small stamped parts) to 0.167 cu ft (larger assemblies).

Warehouse floor area requirements were derived using:

Assumptions:

• Usable warehouse height: 20 feet (standard industrial racking)
• Space utilization efficiency: 70% (accounts for aisles, handling equipment, staging areas)

Formula:

$$\text{Floor Area (sq ft)} = \frac{\text{Total Volume (cu ft)}}{20 \text{ ft} \times 0.70}$$

Results:

Near-client warehouses require capital investment. Using industry-standard construction costs:

• Cost per sq ft: $200/sq ft (pre-engineered metal building with basic racking)

Capital Investment:

Note: Factory warehouse space is integrated into the main facility and does not require separate construction.

Annual inventory carrying cost is estimated at 20% of inventory value (industry standard, covering capital cost, obsolescence risk, insurance, shrinkage).

Assuming average part value of $15/unit:

• Total inventory value: 178,365 units × $15 = $2,675,475
• Annual carrying cost: $2,675,475 × 0.20 = $535,095/year

Breakdown by location:

The three-tier storage strategy provides optimal balance across competing objectives:

1. Service Level Assurance

• Safety stock at factory ensures 99.5% service reliability
• Near-client buffers eliminate last-mile delivery risk
• Dual-location redundancy protects against single-point failures

2. Inventory Investment Efficiency

• Centralized safety stock (factory) avoids duplication at client sites
• Buffer stock sizes precisely matched to client-specific requirements (4h vs. 12h)
• Total inventory investment minimized while meeting service targets

3. Operational Flexibility

• Factory can dynamically adjust production priorities
• Client buffers decouple production from assembly line variability
• Transportation scheduling flexibility (can batch shipments without service impact)

4. Risk Mitigation

• Client-side buffers protect against highway closures, weather delays
• 4-hour buffer (Client A): Covers typical short-duration disruptions
• 12-hour buffer (Client B): Provides overnight autonomy for longer-distance supply chain

Single-tier (Factory-only storage):

• Rejected: No buffer against transportation disruptions; service level vulnerable to last-mile failures
• Would require express shipping or premium logistics to compensate

Two-tier (No factory safety stock):

• Rejected: Requires duplicating safety stock at both client warehouses, increasing total inventory investment by ~30%
• Inefficient use of capital

Consignment inventory at client facilities:

• Rejected: Clients requested minimal on-site footprint; larger warehouses would require client capital investment and floor space

The 99.5% service level (Z=2.576) drives safety stock requirements. Sensitivity analysis:

Finding: Safety stock is highly sensitive to service level. Moving from 99% to 99.5% increases inventory by 3.5%, but guarantees an additional 0.5% service reliability.

Current lead time assumption: 1 week. If production lead time could be reduced:

Finding: Safety stock scales with $\sqrt{L}$. Halving lead time reduces safety stock by 29%, yielding 12.5% total inventory reduction.

Recommendation: Pursue lead time reduction initiatives (e.g., cellular manufacturing, setup time reduction) as a strategic inventory reduction lever.

The storage plan was validated through:

1. Demand-inventory reconciliation: All buffer stock allocations traced to specific product-part relationships
2. Service level verification: Safety stock calculations independently verified using normal distribution tables
3. Physical space reasonableness: Warehouse sizes confirmed feasible for standard industrial construction
4. Cost benchmarking: Inventory carrying cost (20%) validated against industry standards

Data source: Complete detailed results available in results/task12/Task2_Finished_Storage_Capacity_Plan.csv

Key insights for downstream tasks:

• This storage strategy forms the baseline for Task 3 factory design evaluations
• Multi-year storage evolution (Task 4) will scale these formulas with growing demand
• Client buffer requirements remain constant across all organizational designs

Question: Why are near-client warehouses necessary when distance is only ~100 miles (~2 hours)?

Answer: Near-client warehouses are economically justified and operationally essential for three reasons:

1. Service Level Guarantee (SLA compliance)

   • 99.5% OTIF cannot be achieved with 2-hour transit variability
   • Near-client location reduces last-mile transit to 5-15 minutes with minimal variability
   • Benefit: Avoids line-down penalties (~$10k/hour per client)

2. Contractual Autonomy Requirements (client specification)

   • Client A: 4-hour buffer (explicit requirement)
   • Client B: 12-hour buffer (explicit requirement)
   • These buffers must be physically near the assembly line to provide autonomy
   • Benefit: Meets contractual obligations, avoids penalties

3. Transportation Cost Reduction (economic optimization)

   • Without warehouses: 16 daily deliveries × 4-hour round trips = 64 truck-hours/day
   • With warehouses: 2 daily bulk shipments + local shuttles = 8 truck-hours/day
   • Savings: $390k/year in transportation costs
   • Warehouse operating cost: $92k/year
   • Net benefit: +$298k/year (payback in 2 weeks)

Conclusion: Warehouses are not optional; they are a cost-effective necessity driven by service requirements and economic optimization, not just distance.

Question: How were storage quantities determined at each location?

Answer: Quantities are calculated using role-based allocation where each location serves a specific function:

Factory Warehouse (90.3% of total inventory):

Purpose: Absorbs demand variability and enables economic batch production

Near-Client Warehouses (9.7% of total inventory):

Purpose: Decouples transportation lead time from assembly line consumption

Critical insight:

• Factory inventory = $f(\sigma, \text{production cycle})$ — driven by demand uncertainty
• Client inventory = $f(\text{hourly demand}, \text{autonomy time})$ — driven by transit lead time
• No overlap: These serve different failure modes and do not duplicate protection

Validation:

• Total inventory: 178,365 units
• Inventory carrying cost: $535k/year
• Warehouse construction: $13.6k (one-time)
• Total annual cost: ~$548k/year
• Transportation savings: +$390k/year
• Penalty avoidance (99.5% SLA): ~$200k/year (estimated 0.5% failure × $40M line-down risk)
• Net benefit: +$42k/year + risk mitigation

This three-tier strategy is the economically optimal solution that balances inventory investment, service reliability, and operational costs.

Objective: Design and analyze five alternative factory organization models for Year +1, comparing their performance across equipment investment, operating costs, material flow efficiency, flexibility, and scalability.

Required designs (per Casework requirements):

1. Function-based (Functional) — baseline, mandatory
2. Fractal (Holographic) — required alternative
3. Three additional designs from: Process, Parts, Group, Product, or Free-Style

Selected designs for analysis:

• (a) Functional — traditional job-shop paradigm
• (c) Part-Based — dedicated lines per part family
• (f) Fractal — self-contained mini-factories
• (g) Holographic — distributed process network (variant of Fractal)
• (h) Free-Style (Hybrid) — optimized mixed-mode design

Justification for design selection:

Why these five designs?

The selection of these five designs satisfies the Casework requirements while providing maximum analytical insight:

1. (a) Functional Organization — Mandatory baseline

• Required: Explicitly mandated by Casework as the baseline design
• Rationale: Represents FeMoaSa's current paradigm (job-shop tradition)
• Value: Provides benchmark for evaluating alternative designs
• Strategic importance: Must understand current-state performance to justify change

2. (c) Part-Based Organization — Product-focused alternative

• Selected for: High-volume manufacturing efficiency
• Rationale: With 20 parts and clear demand patterns (P1, P14, P19 are high-volume), dedicated lines can exploit economies of scale
• Strategic fit: Aligns with lean manufacturing principles (flow, minimal WIP)
• Hypothesis: Should outperform Functional on lead time and material handling

3. (f) Fractal Organization — Required modular design

• Required: Casework mandates either Fractal or Holographic
• Rationale: Self-contained mini-factories offer operational independence and scalability
• Strategic advantage: Each fractal can produce all 20 parts → maximum flexibility and redundancy
• Innovation potential: Tests whether modular architecture can compete with specialized designs
• Key question: Does redundancy justify capital investment?

4. (g) Holographic Organization — Distributed network alternative

• Selected for: Exploring distributed process replication
• Rationale: Small, focused process centers distributed throughout factory floor
• Differentiation from Fractal: While Fractal groups all processes into centers, Holographic distributes individual process types
• Strategic value: Tests whether process-level distribution (vs. center-level) improves flow
• Hypothesis: May reduce average travel distance below both Functional and Fractal

5. (h) Free-Style Hybrid Organization — Mandatory optimized design

• Required: Explicitly mandated as top-3 or best-performing design
• Rationale: Combines strengths of multiple paradigms to create optimal hybrid
• Design philosophy:
  • High-volume parts (65% demand) → Fractal (f=2) for flexibility + efficiency
  • Medium-volume parts (25% demand) → Part-Based lines for flow optimization
  • Low-volume parts (10% demand) → Functional pool for equipment sharing
• Strategic value: Demonstrates that hybrid designs can outperform pure paradigms
• Innovation: Tests FeMoaSa's willingness to move beyond single-paradigm thinking

Design exclusions and rationale:

• (b) Process Organization: Not selected — would be similar to Functional but with composite equipment (AB, CD, EFG); adds complexity without strategic differentiation
• (d) Group Organization: Not selected — part families already captured by Part-Based design; grouping would be intermediate between Part-Based and Functional
• (e) Product Organization: Not selected — with only 5 products but 20 parts, product-dedicated centers would have low utilization and high equipment duplication

Summary: The selected five designs span the design space from pure paradigms (Functional, Part-Based, Fractal) to hybrid innovation (Free-Style), while testing the frontier hypothesis (Holographic) that distributed replication may outperform both centralization and modularization.

Evaluation framework: Each design is assessed across seven dimensions:

1. Network organization structure
2. Resources and equipment requirements
3. Layouts and spatial configuration
4. Intra-center flows (within production cells)
5. Inter-center flows (between production cells)
6. Key Performance Indicators (KPIs)
7. Costs (capital and operating)

The Functional organization follows the classic job-shop paradigm, grouping equipment by process type into 13 specialized departments (A through M).

Organization model: 2-tier network with internal centres focusing on Functional basis

Operating model: Each needed part is made in centres specializing in respective functions common to all the parts.

Visual representation:

The diagram illustrates the complete network structure from supplier through parts factory to client delivery:

• Supplier → delivers raw materials to Parts Factory
• Parts Factory → organized by functional departments (Process A through M)
• Factory Layout → shows color-coded functional departments within the facility
• Warehouses → Warehouse A (blue) serves Client A, Warehouse B (orange) serves Client B
• Clients → Final delivery points requiring high service levels

Warehouse rationale: The near-client warehouses are necessary to maintain high service levels given the distance from the factory. This 2-tier distribution strategy balances inventory costs with delivery performance.

Network diagram (simplified internal structure):

                         Factory Network
                                |
         +---------------------+----------------------+
         |                     |                      |
   [Receiving] → [Inbound  → [Production      → [Outbound  → [Shipping]
                  Storage]     Departments]      Storage]
                                    |
                    +---------------+---------------+
                    |               |               |
              [Dept A-D]      [Dept E-H]      [Dept I-M]
              (Forming/       (Heat/Surface    (Threading/
               Cutting)        Treatment)       Assembly)

Center missions:

Mission of Parts Factory: To provide all the parts needed for its clients over next 5 years with optimal cost, least effort of operators and resilience to handle sudden changes in demand.

Performance assessment: Performance is assessed based on investment, distance per trip, and equipment utilization.

Structure:

• 13 process departments: Each houses all equipment of one process type
• Centralized routing: Parts travel between departments following their process sequence
• Single management hierarchy: One production control system coordinates all departments
• Shared resources: Material handling equipment, supervision, and support services shared across departments

Organizational chart:

                    Factory Manager
                          |
        +-----------------+-----------------+
        |                 |                 |
   Process Dept A    Process Dept B    ... Process Dept M
   (17 machines)     (21 machines)         (46 machines)

Based on Task 1 capacity planning, the Functional layout requires 386 equipment units distributed across 13 departments.

Equipment allocation:

Support resources:

• Material handling: 15 forklifts, 8 AGVs (automated guided vehicles)
• WIP storage: 12,000 sq ft intermediate buffer zones
• Supervision: 13 shift supervisors (1 per department)

The Functional layout arranges departments in a flow-optimized sequence, but long inter-departmental distances are unavoidable.

Layout dimensions:

• Total floor area: 60,000 sq ft (200 ft × 300 ft)
• Production area: 30,880 sq ft (equipment)
• Aisle/handling: 18,000 sq ft (30%)
• WIP storage: 12,000 sq ft (20%)
• Support/offices: 5,120 sq ft (8.5%)

Spatial arrangement (simplified block layout):

+----------------------------------------------------------+
|  Receiving  |    Dept A-D    |    Dept E-H    | Shipping |
|    Dock     | (Early stages) | (Mid-process)  |   Dock   |
|-------------|----------------|----------------|----------|
| WIP Storage |    Dept I-K    |    Dept L-M    | Finished |
|   (12k sf)  | (Threading/Asm)| (Final Asm)    | Goods    |
+----------------------------------------------------------+

Design rationale:

• Processes arranged in typical routing sequence (A→B→C→D→E...)
• Receiving and shipping at opposite ends to prevent congestion
• WIP storage centrally located for accessibility

Functional Layout Visualization:

Figure 4.1: Final functional organization layout for Year +1 based on peak demand. Shows 13 specialized departments (A through M) arranged in flow-optimized sequence. Total floor area: 60,000 sq ft with 30,880 sq ft production area. This layout is designed to accommodate Year +5 peak demand, with equipment added incrementally in Years 2-5 (1-5 machines per department annually).

Figure 4.1b: Comprehensive functional layout analysis showing departmental organization, equipment distribution, and flow patterns.

Figure 4.2: Inter-departmental flow intensity heatmap. Darker cells indicate higher material flow volumes between departments. Highlights hot paths (e.g., A→D, D→J, J→M) requiring optimized spatial placement.

Figure 4.3: Equipment distribution summary across 13 departments showing machine count, utilization rates, and floor space allocation per process type.

Cost Analysis Visualizations:

Figure 4.4a: Comprehensive cost analysis breakdown showing capital investment, operating costs, and cost drivers for functional organization.

Figure 4.4b: Cost and KPI dashboard displaying key performance metrics including cost per unit, cost per square foot, and ROI indicators.

Figure 4.4c: Cost efficiency matrix comparing process-level costs, identifying high-cost departments and optimization opportunities.

Figure 4.4d: Enhanced cost analysis with multi-dimensional breakdown including labor, material handling, maintenance, and overhead costs.

Figure 4.4e: Process-by-process cost comparison highlighting cost variation across departments A-M and identifying cost reduction targets.

Figure 4.4f: Investment timeline analysis showing phased capital deployment and expected ROI over 5-year planning horizon.

Figure 4.4g: Return on investment (ROI) analysis comparing initial capital outlay against projected operating savings and payback period calculation.

Within each department, material movement is minimal and controlled.

Flow characteristics:

• Queue-based flow: Parts wait in queues before each machine
• Batch movement: Parts move in batches between queue and machine
• FIFO discipline: First-in-first-out for most departments
• Priority override: Expedited orders can jump queue

Average intra-department metrics:

Total intra-department travel: Minimal, estimated at ~30,000 ft/week aggregated across all departments.

Inter-departmental flows dominate material handling in the Functional layout, creating significant complexity.

Flow matrix analysis (from Flow_Matrix_Summary.csv):

Key observations:

• High trip volume: 952,500 inter-departmental trips per week
• Long average distance: 47 feet per trip (department-to-department)
• Bottleneck congestion: Departments D, J, M generate heavy traffic
• Cross-traffic conflicts: Multiple parts competing for same aisles

Annual material travel: 1,250,000 km/year — benchmark for comparison to other designs.

Quantitative KPIs:

Qualitative assessment:

• Strengths:

  • High equipment utilization (97.8%)
  • Economies of scale within departments
  • Specialized supervision per process type
  • Flexible capacity allocation

• Weaknesses:

  • Excessive material handling (1.25M km/yr)
  • Long lead times due to queue waits
  • Complex production control
  • Poor schedule adherence (87%)
  • High WIP inventory

Capital investment:

Annual operating costs:

Cost drivers:

• Material handling: $4,450,000/year (20.2% of operating costs)
• Long travel distances drive handling labor and equipment costs
• Schedule complexity drives WIP inventory and carrying costs

The Part-Based organization creates dedicated production lines for each part or part family, grouping all necessary processes within each line.

Network diagram:

                       Factory Network
                              |
         +--------------------+--------------------+
         |                    |                    |
   [Receiving] → [Part Family Lines (5)] → [Shipping]
                         |
        +----------------+----------------+
        |        |       |       |        |
    [Line 1] [Line 2] [Line 3] [Line 4] [Line 5]
     3 parts  4 parts  3 parts  3 parts  7 parts
    (All ops) (All ops) (All ops) (All ops) (All ops)

Center missions:

Structure:

• 5 part family lines: Grouped by process similarity and volume

  • Line 1: High-volume stamped parts (P1, P14, P19) — 30% of demand
  • Line 2: Threaded components (P7, P12, P18, P20) — 25% of demand
  • Line 3: Assembled parts (P11, P9, P4) — 18% of demand
  • Line 4: Surface-finished parts (P2, P16, P6) — 15% of demand
  • Line 5: Low-volume specialty (P3, P5, P8, P10, P13, P15, P17) — 12% of demand

• Dedicated resources: Each line has its own equipment, operators, supervision

• Decentralized control: Line managers have full production authority

• Minimal inter-line flow: Parts stay within their assigned line

Organizational chart:

              Factory Manager
                    |
    +---------------+---------------+
    |       |       |       |       |
  Line 1  Line 2  Line 3  Line 4  Line 5
  Manager Manager Manager Manager Manager
    |       |       |       |       |
  (Equip) (Equip) (Equip) (Equip) (Equip)
  (Ops)   (Ops)   (Ops)   (Ops)   (Ops)

Part-Based layout requires 430 equipment units (+11.4% vs. Functional) due to dedicated equipment per part family.

Equipment requirements by part (top 5 high-demand parts):

Overall equipment allocation by process family:

Personnel requirements:

Layout Development Methodology:

A systematic 12-step process was employed:

1. Flow Matrix Computation: Inter-equipment flows quantified and ranked by volume
2. Flow Visualization: Proportionally scaled arrows (thickness ∝ flow intensity)
3. Equipment Clustering: Processors organized by quantity and spatial requirements
4. Scale Calibration: Grid cells set to 10:1 scale (1 cell = 100 sq ft = 10 ft × 10 ft)
5. Center Mapping: Equipment centers mapped based on aggregate spatial requirements
6. Skeletal Layout: Highest-flow centers positioned in closest proximity
7. Flow Optimization: Lower-volume flows optimized using planar diagrams
8. Integration: Equipment centers integrated with circulation space
9. Coordinate System: Euclidean distance metrics applied using x-y coordinates
10. Center Alignment: All process centers vertically aligned for horizontal flow (constant Y)
11. Distance Calculation: Sequential distances: d_ij = |X_j - X_i|
12. Processor Layout: Individual equipment units plotted within each center

Equipment spatial envelope and overlap strategy:

Total overlap savings: ~2,472 sq ft across all part families

Row-Column Heuristic (for N machines per process):

• Rows = ⌈√(2N)⌉ (creates taller layout for unidirectional flow)
• Columns = ⌈N / Rows⌉
• Rationale: Minimizes cross-flow, optimizes for left-to-right material movement

Example: Process I with 7 machines

• Rows = ⌈√(14)⌉ = 4
• Columns = ⌈7/4⌉ = 2
• Grid: 4 × 2 = 8 slots (7 machines + 1 empty)

Layout dimensions:

• Total floor area: 296,064 sq ft (Year +1)
• Effective production area: 245,000 sq ft (equipment + overlap)
• Aisle/handling: 28,800 sq ft (9.7%)
• WIP buffers: 14,264 sq ft (4.8%)
• Support areas: 8,000 sq ft (2.7%)

Horizontal flow configuration:

All part lines follow left-to-right flow with center alignment at Y_ref = max(H)/2.

Example: P1 production line

• Process sequence: B → A → B2 → C → D → I → J
• Total width: 224 ft (0 → 224)
• Maximum height: 216 ft (set by Process J)
• Footprint: 48,384 sq ft per line
• Sequential distances: 32 + 32 + 26 + 26 + 33 + 35 = 184 ft total travel per unit

Spatial arrangement (simplified):

+------------------------------------------------------------+
| Line 1: High-Volume Stamped (P1, P14, P19)                |
| [A]→[B]→[C]→[D]→[E]→[F]→[G]→[H]→[I]→[J]→[K]→[L]→[M]     |
|------------------------------------------------------------|
| Line 2: Threaded Components (P7, P12, P18, P20)           |
| [A]→[B]→[C]→[D]→[E]→[F]→[G]→[H]→[I]→[J]→[K]→[L]→[M]     |
|------------------------------------------------------------|
| Line 3: Assembled Parts (P11, P9, P4)                     |
| [A]→[B]→[C]→[D]→[E]→[F]→[G]→[H]→[I]→[J]→[K]→[L]→[M]     |
|------------------------------------------------------------|
| Line 4: Surface-Finished (P2, P16, P6)                    |
| [Processes as needed]                                      |
|------------------------------------------------------------|
| Line 5: Low-Volume Specialty (7 parts)                    |
| [Flexible cell with all processes]                        |
+------------------------------------------------------------+

Design rationale:

• Each line is self-contained with sequential flow
• Minimal backtracking within lines
• Parallel lines enable simultaneous production of all part families

Part-Based Layout Visualization:

Figure 4.8a: Part-based organization layout showing dedicated production lines optimized for specific part families. Each line contains sequential process flow tailored to its assigned parts.

Figure 4.8b: Comprehensive part-based layout dashboard with equipment distribution, utilization metrics, and flow analysis across all production lines.

Figure 4.8c: Compact spatial arrangement of part-based production lines. Each line is color-coded by part family, demonstrating the focused factory concept with minimal cross-line material movement.

Figure 4.8d: Year 1 part-based organization dashboard showing key performance indicators, equipment allocation, and space utilization metrics.

Figure 4.8e: Detailed schematic view of Year 1 part-based layout with process flow paths and material handling routes.

Figure 4.8f: Top 10 parts analysis showing highest-volume parts, their production lines, and contribution to total throughput.

Figure 4.8g: Top 10 parts production visualization with demand volumes, equipment requirements, and layout optimization.

Cost Analysis Visualizations:

Figure 4.9a: Part-based comprehensive analysis dashboard integrating cost, performance, and efficiency metrics.

Figure 4.9b: Detailed cost breakdown for part-based organization showing capital investment, operating costs, and cost per unit analysis.

Figure 4.9c: Cost and KPI dashboard highlighting financial performance metrics, ROI indicators, and cost efficiency benchmarks for part-based design.

Within each production line, flow is highly streamlined.

Flow characteristics:

• Sequential flow: Parts move linearly through process sequence
• Minimal backtracking: Process arrangement matches routing
• Short move distances: Average 12 ft between adjacent processes
• Conveyor-based: Lines 1-3 use powered conveyors
• Manual transfer: Lines 4-5 use operator-pushed carts

Intra-line metrics:

Total intra-line travel: ~18,000 ft/week (40% reduction vs. Functional).

Inter-line flows are minimal by design — parts rarely transfer between lines.

Flow matrix:

Key observations:

• 99.7% reduction in inter-center travel vs. Functional (1,250 km → 3 km)
• Almost all part production contained within assigned line
• Exceptional cases handled manually with minimal impact

Quantitative KPIs:

Qualitative assessment:

• Strengths:

  • Minimal material handling (99.7% reduction)
  • Short lead times (2.9 days vs. 4.2)
  • High schedule reliability (96%)
  • Simple production control per line
  • Low WIP inventory

• Weaknesses:

  • Equipment duplication (+34 units, +$6.8M)
  • Lower utilization (91.6% vs. 97.8%)
  • Higher setup overhead (line changeovers)
  • Lines 4-5 underutilized (85-89%)

Capital investment:

Annual operating costs:

Cost drivers:

• Higher capital: Equipment duplication adds $6.8M
• Lower operating: Material handling savings of $2.72M/year
• Payback: Operating savings yield 2.5-year payback on additional capital

The Fractal organization creates identical, self-sufficient mini-factories (fractals), each capable of producing the full range of 20 parts.

Network diagram:

                    Factory Network
                          |
         +----------------+----------------+
         |                |                |
    [Center 1]       [Center 2]       [Center 3]
    33.3% load       33.3% load       33.3% load
         |                |                |
    [All A-M ops]    [All A-M ops]    [All A-M ops]
    [All 20 parts]   [All 20 parts]   [All 20 parts]
         |                |                |
         +----------------+----------------+
                          |
                   [Shared Shipping]

Center missions:

Fractal configuration analysis:

f=3 configuration selected based on:

• Optimal balance: 4.1% overhead acceptable for 67% redundancy
• High utilization: 93.1% maintains excellent equipment efficiency
• Manageable complexity: 3 centers easier to coordinate than 4-5
• Scalability: Can evolve to f=5 or f=7 as demand grows
• Load balancing: 33.3% per center provides clear allocation

Alternative consideration (f=4):

• Lower overhead (3.6% vs. 4.1%)
• Higher redundancy (75% vs. 67%)
• Slightly lower utilization (90.8% vs. 93.1%)
• Better for demand fluctuations (25% per center easier to balance)

Structure (f=3 configuration):

• 3 fractal centers: Each is a complete mini-factory
• Load balancing: Demand split equally across centers (33.3% each)
• Full process coverage: Each center has all 13 process types (A-M)
• Autonomous operation: Each center operates independently
• Redundancy: If one center fails, others absorb its load

Organizational chart:

          Factory Manager
                |
    +-----------+-----------+
    |           |           |
 Center 1    Center 2    Center 3
 Manager     Manager     Manager
    |           |           |
[13 process] [13 process] [13 process]
 types A-M    types A-M    types A-M

Fractal philosophy:

• Holographic principle: Each part (center) reflects the whole (factory)
• Modularity: Add/remove centers without disrupting others
• Scalability: Growth = replicate centers
• Resilience: System degrades gracefully with center failures

Fractal layout (f=3) requires 402 equipment units (+4.1% vs. Functional) distributed across 3 identical centers.

Equipment allocation per center:

Center-level utilization:

Redundancy and flexibility:

• 66.7% redundancy: Any 2 centers can handle 100% load at 140% utilization (with overtime)
• Maintenance windows: Can service one center while others operate
• Demand surge capacity: Temporarily overload one center or add 4th center

Each fractal center is identically laid out with co-located processes minimizing internal travel.

Single center dimensions:

• Floor area per center: 18,000 sq ft (120 ft × 150 ft)
• Equipment footprint: 10,720 sq ft (134 units × 80 sq ft)
• Aisle/handling: 4,680 sq ft (26%)
• WIP buffer: 1,800 sq ft (10%)
• Local office: 800 sq ft (supervisor + IT)

Total factory dimensions:

• Total floor area: 58,000 sq ft (3 centers + shared support)
  • Center 1: 18,000 sq ft
  • Center 2: 18,000 sq ft
  • Center 3: 18,000 sq ft
  • Shared receiving/shipping: 2,500 sq ft
  • Central maintenance: 1,500 sq ft

Spatial arrangement (simplified):

+--------------------------------------------------------+
|  Receiving  |  Center 1   |  Center 2   |  Center 3  |
|    Dock     | [All A-M]   | [All A-M]   | [All A-M]  |
|             | processes   | processes   | processes  |
|-------------|-------------|-------------|------------|
| Maintenance | Local WIP/  | Local WIP/  | Local WIP/ |
|  Workshop   | Buffer      | Buffer      | Buffer     |
|             |-------------|-------------|------------|
|  Shipping   | Each center is a self-contained mini-factory  |
|    Dock     | with identical process layout (A→B→...→M)  |
+--------------------------------------------------------+

Within-center layout (each center uses cellular arrangement):

Center 1 Internal Layout:
+------------------+
| Input Queue      |
|   ↓              |
| [A][B][C]        |
|   ↓  ↓  ↓        |
| [D][E][F][G]     |
|   ↓  ↓  ↓  ↓     |
| [H][I][J]        |
|   ↓  ↓  ↓        |
| [K][L][M]        |
|   ↓              |
| Output Queue     |
+------------------+

Design rationale:

• Processes arranged in typical routing sequence within each center
• Minimal backtracking (flow follows cell layout)
• Centers isolated to prevent inter-center traffic

Fractal Layout Visualization:

f=2 Configuration:

Figure 4.5a: Fractal organization with f=2 configuration. Two identical mini-factories with 50% capacity each, offering maximum operational independence and redundancy.

f=3 Configuration (Selected Design):

Figure 4.5b: Fractal organization with f=3 configuration. Three identical mini-factories (Fractal Centers 1-3) arranged in symmetric layout. Each fractal contains all 13 processes scaled to ~33% of total capacity, enabling independent production of all 20 parts.

f=4 Configuration:

Figure 4.5c: Fractal organization with f=4 configuration. Four mini-factories with 25% capacity each, providing enhanced flexibility and scalability.

Flow Matrix Analysis:

Figure 4.6a: Flow matrix for f=2 configuration showing minimal inter-center flows.

Figure 4.6b: Flow matrix for f=3 configuration demonstrating highly localized material flows within each fractal center.

Figure 4.6c: Flow matrix for f=4 configuration with maximum flow distribution across centers.

Comparative Analysis:

Figure 4.7: Comparison of fractal configurations (f=2, f=3, f=4, f=5). Charts show trade-offs between equipment investment, floor space, material handling efficiency, and operational flexibility across different fractal counts.

Figure 4.8: Equipment distribution across fractal centers. Bar charts illustrate how the 13 process types are allocated across each fractal, demonstrating the balanced workload distribution that enables operational independence.

Figure 4.9: Equipment comparison across f=2, f=3, f=4, f=5 configurations showing total equipment requirements and efficiency trade-offs.

Figure 4.10: Efficiency frontier analysis demonstrating optimal fractal configuration selection based on cost, flexibility, and performance metrics.

Figure 4.11: Detailed process-level cost breakdown across different fractal configurations, identifying cost drivers and optimization opportunities.

Within each fractal center, material flow is highly efficient due to co-location.

Flow characteristics:

• Cellular flow: Parts follow U-shaped or serpentine paths
• Short distances: Average 8 ft between process steps
• Gravity/roller conveyors: Minimal powered handling
• Kanban pull: Visual signals trigger production
• One-piece flow: Approaching single-unit transfer for high-volume parts

Intra-center metrics (per center):

Total intra-center travel (all 3 centers): ~15,600 ft/week (48% reduction vs. Functional).

The defining advantage of Fractal: inter-center flows are essentially zero.

Flow matrix:

Key observations:

• 99.99% reduction in inter-center travel vs. Functional (1,250 km → 0.09 km)
• Virtually no inter-center traffic
• Each center is a complete, self-contained production unit
• Exception handling only (quality issues, tooling moves)

Traffic density comparison:

Quantitative KPIs:

Qualitative assessment:

• Strengths:

  • Near-zero material handling complexity
  • Extremely short lead times (2.4 days)
  • Highest schedule reliability (98%)
  • Excellent redundancy (66.7%)
  • Simple expansion path (add 4th center)
  • Isolated failure modes (center failures don't cascade)
  • Lowest WIP inventory ($246k)
  • Modular organization (easy to manage)

• Weaknesses:

  • Slight equipment overhead (+4.1%, +16 units)
  • Slightly lower utilization (93.1% vs. 97.8%)
  • Requires 3x supervision structure
  • Potential idle capacity if demand < 67% (can't fully utilize one center)

Strategic advantages:

• Scalability: Adding capacity is trivial (build 4th identical center)
• Risk mitigation: One center failure = 66.7% capacity retained
• Simplicity: Each center managed independently
• Flexibility: Can dedicate centers to specific clients or shift patterns

Capital investment:

Annual operating costs:

Cost analysis summary:

Key findings:

• Minimal capital premium: +1.4% vs. Functional
• Significant operating savings: -14.9% annually
• Rapid payback: Additional capital recovered in 4.6 months
• 5-year NPV advantage: $15.1M savings vs. Functional

The Holographic organization is a variant of the Fractal design with additional distribution and networking principles.

Network diagram:

                  Factory Network
                        |
            Central Scheduler (Dynamic)
                        |
        +-------+-------+-------+-------+
        |       |       |       |       |
    [Node 1] [Node 2] [Node 3] [Node 4]
    Primary:  Primary:  Primary:  Primary:
    P1-P5    P6-P10    P11-P15   P16-P20
        |       |       |       |
    Secondary coverage: Can produce any part when needed
        |       |       |       |
        +-------+-------+-------+-------+
                        |
                 [Shared Shipping]

Center missions:

Structure (h=4 configuration):

• 4 holographic nodes: Similar to fractal centers but with inter-node collaboration
• Partial redundancy: Nodes specialize in certain parts but can cross-cover
• Network coordination: Central scheduler allocates work across nodes dynamically
• Distributed inventory: Each node holds inventory for its specialty parts

Key difference from Fractal:

• Fractal: Identical centers, static load balancing (33.3% each)
• Holographic: Specialized nodes, dynamic load balancing (varies by demand)

Organizational chart:

       Factory Manager + Central Scheduler
                    |
    +-------+-------+-------+-------+
    |       |       |       |       |
  Node 1  Node 2  Node 3  Node 4
 (Special)(Special)(Special)(Special)
 Parts 1-5 Parts 6-10 Parts 11-15 Parts 16-20
   |       |       |       |
[Partial] [Partial] [Partial] [Partial]
coverage  coverage  coverage  coverage

Holographic layout (h=4) requires 400 equipment units (+3.6% vs. Functional).

Equipment allocation:

Coverage matrix (which nodes can produce which parts):

Legend: ● = Primary producer, ○ = Can produce if needed, — = Cannot produce

Average redundancy: 62.5% of parts can be produced by 2+ nodes.

Key Characteristics:

The holographic layout divides the traditional design canvas into a number of flexible visual fields. These fields present the whole content in a way that emphasizes dynamic flow and creativity rather than rigid alignment. In contrast to designs constrained within a single, rigid grid, the holographic approach offers:

• Flexible: Elements positioned intuitively and creatively without constraints of columns or rows
• Visual Flow: The visual path is shaped by the design itself rather than simple linear progression
• Dynamic: Elements can be added or removed as the design is not confined by a static grid or fixed template
• Engaging: Content can be overlapped, angled, or varied in size for visual interest and impact

Each node is designed with partial process coverage tailored to its specialty parts, distributed across the factory floor in a holographic pattern.

Total factory dimensions:

• Total floor area: 60,000 sq ft
• Layout dimensions: 300 ft × 200 ft optimized for flow-based adjacency placement
• Organization: 4 distributed holographic nodes + shared logistics
• Design approach: Uses best practices to reduce area and flow, improving KPIs by incorporating elements from all layout types

Within-node layout:

• Each node has processes needed for its primary parts
• Nodes share certain low-utilization processes via central pool
• More complex than Fractal (asymmetric node designs)
• Distributed across factory floor in non-linear arrangement

Holographic Layout Visualization:


Figure 4.12: Holographic organization layout showing distributed process nodes across 60,000 sq ft factory floor. Color-coded zones indicate primary part assignments (P1-P4 visible in layout) with flexible visual flow and dynamic element placement.

Development Process Visualizations:

Figure 4.13a: Holographic layout development Step 1 - Initial process placement and primary node definition.

Figure 4.13b: Holographic layout development Step 2 - Equipment distribution and capacity allocation.

Figure 4.13c: Holographic layout development Step 4 - Flow path optimization and inter-node connectivity.

Figure 4.13d: Holographic layout development Step 5 - Fine-tuning spatial arrangement for minimal travel distance.

Figure 4.13e: Holographic layout development Step 8 - Final adjustments and validation of holographic network.

Figure 4.13f: Holographic layout development Step 10 - Complete holographic organization with all nodes integrated.

Actual Flow Metrics (from holographic layout analysis):

• Total Trips: 755,003 trips/year
• Distance per trip: 35 ft/trip (optimized through holographic node distribution)
• Total annual distance: 755,003 trips × 35 ft = 26,425,105 ft/year
• Annual travel distance: 26,425,105 ft × 0.0003048 km/ft ≈ 8,050 km/year

Intra-node flows: Efficient cellular flow within nodes, similar to fractal centers.

Inter-node flows: Higher than Fractal due to cross-coverage and shared resources, but significantly optimized through holographic distribution.

Flow matrix:

Comparative Analysis:

Key observation: Holographic achieves 99.36% reduction vs. Functional while maintaining dynamic flexibility. The 35 ft average distance per trip demonstrates highly efficient spatial organization through strategic node placement, though with more inter-node movement than pure Fractal design.

Trade-offs:

• +Flexibility: Can dynamically shift work between nodes
• +Adaptability: Responds well to demand volatility
• +Excellent material flow: 8,050 km/year (99.36% reduction vs. Functional)
• -Complexity: Requires central scheduling system
• -Higher inter-node travel: 8,050 km vs. 0.09 km (Fractal), but still highly efficient

Operator Requirements:

Figure 4.14: Holographic organization operator allocation across 4 nodes showing staffing requirements by skill level (C1, C2, C3) and shift distribution.

Cost Analysis:


Figure 4.15: Holographic organization comprehensive cost breakdown including capital investment, operating costs, labor, material handling, and facility costs.

Summary Cost Table:

Note: The holographic design achieves comparable floor area (60,000 sq ft, same as functional) while providing enhanced organizational capabilities:

1. Distributed node placement: Nodes spread across factory floor for optimal flow without area penalty
2. Efficient spatial utilization: Holographic arrangement maintains compact footprint
3. Inter-node optimization: Strategic placement minimizes material travel
4. Balanced design: Achieves redundancy benefits without excessive space requirements

Assessment: Holographic design achieves operational flexibility and excellent material handling efficiency (99.36% reduction vs. Functional) within standard factory footprint. The 35 ft/trip average demonstrates highly effective spatial organization.

Verdict: Holographic is suitable when:

• Factory space is available at reasonable cost
• Dynamic flexibility and demand variability management are priorities
• Material handling efficiency is valued over absolute space utilization

The Free-Style design is a pragmatic hybrid combining best elements of Fractal and Part-Based approaches.

Network diagram:

                  Factory Network
                        |
        +---------------+---------------+
        |               |               |
   [Zone 1:        [Zone 2:        [Zone 3:
   Fractal f=2]    Part Lines]     Functional]
   65% demand      25% demand      10% demand
        |               |               |
   [Center A]      [Line 1-3]     [Shared A-M]
   [Center B]       Each line      All 13 ops
   All A-M ops      dedicated       Low-volume
        |               |               |
        +---------------+---------------+
                        |
                 [Shared Shipping]

Center missions:

Structure:

• Zone 1 (Fractal): 2 fractal centers for high-volume, high-variety parts (P1, P7, P12, P14, P18, P19, P20) — 65% of demand
• Zone 2 (Part-Based): Dedicated lines for mid-volume parts (P2, P4, P5, P9, P11, P16) — 25% of demand
• Zone 3 (Functional): Shared processes for low-volume specialty parts (P3, P6, P8, P10, P13, P15, P17) — 10% of demand

Design philosophy:

• High-volume/high-variety → Fractal (efficiency + redundancy)
• Mid-volume/stable → Part-Based (flow efficiency)
• Low-volume/specialty → Functional (equipment sharing)

Organizational chart:

            Factory Manager
                  |
    +-------------+-------------+
    |             |             |
Zone 1: Fractal  Zone 2:      Zone 3:
(2 centers)      Part Lines   Functional
  |             (3 lines)     (Shared pool)
Center A        Line 1         [Shared A-M]
Center B        Line 2
                Line 3

Operating Assumptions:

• Schedule: 5 days/week, 2 shifts/day (recommended), 8 hours/shift
• Efficiency: 90%
• Reliability: 98%
• Effective Availability: 88.2% (0.90 × 0.98)
• Availability per equipment:
  • 1 shift: 2,116.8 minutes/week
  • 2 shifts: 4,233.6 minutes/week

Key Characteristics:

The freestyle layout divides the traditional design canvas into a number of flexible visual fields. These fields are able to present the whole content in a way that they place emphasis on dynamic flow and creativity rather than rigid alignment. In contrast to having a design constrained within a single, rigid grid:

• Flexible: The design is composed of elements that can be positioned intuitively and creatively without the constraints of columns or rows
• Visual Flow: As elements are introduced, the visual path is shaped by the design itself rather than a simple linear progression
• Dynamic: Elements can be added or removed, as the design is not confined by a static grid or fixed template
• Engaging: Content can be overlapped, angled, or varied in size for visual interest and impact

Freestyle layout requires 400 equipment units (+3.6% vs. Functional 386 units).

Design Philosophy:

Used the best way to reduce the area and flow to improve the KPIs in the freestyle, incorporating the best of all layouts individually. This freestyle combines both the part-based layout and the functional layout, integrating them to reduce flow complexity and minimize the need for multiple separate production lines.

Equipment allocation by zone:

Number of Operators: 541 operators total across all three zones

Rationale:

• Zone 1 (Fractal): High utilization due to volume concentration
• Zone 2 (Part-Based): Dedicated lines avoid handling costs and reduce complexity
• Zone 3 (Functional): Equipment sharing prevents underutilization for low-volume parts

Total factory dimensions:

• Total floor area: 60,000 sq ft
  • Zone 1 (Fractal): 30,000 sq ft (2 centers)
  • Zone 2 (Part-Based): 20,000 sq ft (dedicated lines)
  • Zone 3 (Functional): 8,000 sq ft (shared pool)
  • Central logistics: 2,000 sq ft
• Layout dimensions: 300 ft × 200 ft
• Design approach: Incorporates best of all layout types individually
• Integration strategy: Combines part-based and functional layouts to reduce flow and complexity

Spatial arrangement:

+----------------------------------------------------------+
| Zone 1: Fractal (65% of demand)                         |
|  +-----------------------+  +-----------------------+    |
|  | Center A: All A-M     |  | Center B: All A-M     |    |
|  | processes             |  | processes             |    |
|  +-----------------------+  +-----------------------+    |
|----------------------------------------------------------|
| Zone 2: Part-Based Lines (25% of demand)                |
|  [Line 1: P2, P16]  [Line 2: P4, P9]  [Line 3: P5, P11] |
|----------------------------------------------------------|
| Zone 3: Functional Pool (10% of demand)                 |
|  [Shared process pool for low-volume P3,P6,P8,P10...]   |
+----------------------------------------------------------+

Free-Style Hybrid Layout Visualization:


Figure 4.16: Free-style hybrid organization final layout across 60,000 sq ft integrating three design paradigms. Shows spatial distribution of zones with dynamic flow paths and creative element positioning for optimal material handling.

Distance Analysis:


Figure 4.17: Free-style organization distance matrix showing inter-zone and intra-zone travel distances. Matrix reveals optimized spatial relationships between functional, part-based, and fractal zones.

Part-Based Component Layouts (Zone 2 detail):

The part-based zone incorporates dedicated production lines optimized for specific high-volume parts:

Note: Detailed part-based line configurations for P1, P2, P4, P7 are documented in separate analysis files (Part Based P1.xlsx, Part Based P2.xlsx, Part Based P4.xlsx, Part Based P7.xlsx) showing equipment allocation, process flow, and line balancing for each part family.

Actual Flow Metrics (from freestyle layout analysis):

• Total trips per year: 755,003 trips/year (combined fractal + part-based movements)
• Average distance per trip: 40 feet (weighted average: fractal zones ~25 ft, part-based zones ~55 ft)
• Total annual travel distance: 9,213 km/year (755,003 trips × 40 ft × 0.0003048 km/ft)
• Annual flow efficiency: Optimized through combination of layout strategies

Intra-zone flows: Excellent within each zone due to zone-specific optimization.

Inter-zone flows: Moderate, with some parts transferring between zones for secondary operations.

Flow metrics:

Distance between each center (inter-zone distances):

Spatial optimization ensures that related zones are positioned to minimize material travel. The 40 ft/trip average indicates highly efficient hybrid layout integrating three organizational paradigms.

Assessment:

• Combines best material handling characteristics from all three organizational types
• 40 ft/trip demonstrates excellent spatial integration of fractal, part-based, and functional zones
• Significantly reduces flow complexity compared to pure functional organization (99.26% reduction)
• Balances flow efficiency with organizational flexibility

Strengths:

• Balanced approach: No single critical weakness across all metrics
• Good equipment utilization: 90.8% demonstrates effective capacity planning
• Efficient material handling: 40 ft/trip average shows highly optimized spatial layout
• Excellent flow reduction: 9,213 km/year (99.26% reduction vs. Functional)
• Flexible for mixed demand patterns: Combines advantages of multiple organizational types
• Reduced complexity: Compared to pure functional, significantly simplifies material flow
• Pragmatic design: Incorporates best practices from fractal and part-based approaches
• Compact footprint: 60,000 sq ft comparable to other efficient designs

Weaknesses:

• More complex to manage: Three distinct zone types require different management approaches
• Inter-zone coordination overhead: Requires coordination between fractal, part-based, and functional zones
• Higher operator count: 541 operators higher than other designs due to distributed operations
• Less elegant than pure Fractal: Hybrid nature adds organizational complexity

Overall Cost Analysis:


Figure 4.18: Free-style organization comprehensive cost matrix showing capital investment, operating costs, labor, equipment, facility costs, and total cost of ownership across all three zones.

Cost Summary:

Investment Breakdown:

• Equipment investment: 400 units × average $175k/unit ≈ $70.0M
• Building cost: 60,000 sq ft × $250/sq ft = $15.0M
• Material handling systems: Advanced systems for 3 zones ≈ $2.3M
• Installation and commissioning: ~$1.2M
• Contingency and other costs: ~$0.9M
• Total investment: $89,400,000

Operating Cost Drivers:

• Labor: 541 operators across 3 zones (highest among all designs)
• Material handling: 40 ft/trip average, efficient handling costs
• Maintenance: Distributed across 3 different zone types
• Supervision: Requires management for fractal, part-based, and functional zones

Cost Analysis Insights:

The Freestyle hybrid achieves competitive investment ($89.4M, only 2.2% higher than Functional) while delivering significantly enhanced performance:

1. Comparable floor area: 60,000 sq ft matches functional baseline
2. Higher operator efficiency: 541 operators with 90.8% utilization across diverse zones
3. Sophisticated material handling: Three-zone integration optimized to 40 ft/trip average
4. Balanced cost structure: Modest capital premium justified by operational flexibility

Verdict:

Freestyle hybrid represents a moderate-investment, high-flexibility strategy suitable when:

• Diverse product mix requires multiple organizational paradigms
• Operational flexibility across varied demand patterns is critical
• Modest capital premium ($1.9M over Functional) acceptable for enhanced capabilities
• Organization has expertise managing complex multi-zone operations

Not recommended when:

• Simpler single-paradigm design meets all requirements
• Lower organizational complexity is preferred
• Pure fractal scalability is more valuable than multi-zone flexibility

Table 4.1: Five Factory Designs — Comprehensive Comparison

Weighted scoring (weights reflect FeMoaSa strategic priorities):

Sensitivity analysis: Fractal remains #1 ranked across all reasonable weighting scenarios.

Comprehensive Design Comparison Visualizations:

Figure 4.9: Multi-dimensional comparison of five factory organization designs across capital investment, operating costs, material flow efficiency, scalability, and operational performance metrics.

Radar chart data (from Fractal_Radar_Chart_Data.csv):

Figure 4.10: Radar chart comparison showing Fractal design (f=3) achieves near-maximum scores across all six evaluation dimensions (operating cost, capital efficiency, material flow, scalability, lead time, redundancy), demonstrating its balanced excellence.

Cost-efficiency analysis:

Figure 4.11: Process-by-process cost breakdown for fractal organization. Shows how costs are distributed across the 13 process types, with fractals achieving economies of scale while maintaining operational independence.

Selected Design: Fractal Organization (f=3 configuration)

Justification:

1. Lowest 5-year total cost: $182.7M (saves $15.1M vs. Functional)
2. Best operating cost: $18.8M/year (14.9% savings vs. Functional)
3. Minimal material handling: 0.09 km/year (99.999% reduction vs. Functional)
4. Shortest lead time: 2.4 days (43% faster than Functional)
5. Highest redundancy: 66.7% (any 2 of 3 centers can handle full load)
6. Best scalability: Modular growth path (add 4th center for expansion)
7. Modest capital premium: Only +$1.3M vs. Functional (+1.4%), recovered in 4.6 months

Strategic alignment:

• Supports FeMoaSa's JIT service promise (2.4-day lead time)
• Enables reliable 99.5% service level (98% schedule adherence)
• Provides resilience against disruptions (66.7% redundancy)
• Future-proof for Years +2 to +5 expansion (Task 4)
• Simplifies management (autonomous centers)

Implementation path:

• Phase 1 (Months 1-6): Build Center 1, migrate 33% of production
• Phase 2 (Months 7-12): Build Center 2, migrate another 33%
• Phase 3 (Months 13-18): Build Center 3, migrate final 33%
• Phase 4 (Months 19-24): Decommission old Functional layout

Risk mitigation: Phased implementation allows fallback to Functional if issues arise during transition.

Objective: Develop evolutionary adaptation plans for three selected factory designs (Functional, Fractal, Free-Style) across Years +2 to +5, demonstrating how each design accommodates demand growth and product mix changes.

Selected designs for evolution analysis:

1. Functional — baseline for comparison
2. Fractal (f=3) — recommended design from Task 3
3. Free-Style (Hybrid) — best non-fractal alternative

Methodology: For each year (+2 through +5):

1. Calculate updated part demand from product forecasts
2. Determine equipment requirements per process (A-M)
3. Design layout adaptations and expansion plans
4. Quantify relayout activities (equipment moves, installations)
5. Calculate storage capacity requirements
6. Assess KPIs: costs, lead times, material flow, utilization
7. Evaluate cumulative capital investment

Year +1 baseline: 5 products (A1, A2, A3, B1, B2) — 420,000 units/year

Years +2 to +5: Product expansion and growth

Key insights:

• Major expansion in Year +2 (+47%) driven by 3 new product introductions
• Steady organic growth Years +3 to +5 (+15% cumulative)
• Near-doubling of volume by Year +5

Aggregate part demand summary:

Fastest-growing parts (Year +5 vs. Year +1):

Key observations:

• New products B3, B4 drive disproportionate growth in certain parts
• Parts P8, P15 experience 4x+ growth — major capacity expansion required
• Some parts (P14, P16) grow modestly (<1.3x) — stable demand

Total capacity requirement growth:

Process-level bottleneck evolution (Year +5 requirements):

Key findings:

• Equipment requirements nearly double by Year +5 (+99%)
• Bottleneck processes (D, J, M) require 100+ units each by Year +5
• Utilization remains high (>98%) indicating efficient capacity planning

Network diagram evolution (Years +1 to +5):

Year +1:                    Year +2-5:
    Factory                     Factory (Expanded)
        |                            |
  [13 Depts A-M]      [13 Depts A-M] (Each grows)
  386 units total     560→583→619→767 units
        |                            |
   [Shipping]                  [Shipping]

Center missions by year:

Network evolution notes:

• Organization type preserved: Function-based departmentalization maintained throughout
• No structural changes: Same 13 departments, only capacity scaling
• Incremental expansion: Each year adds equipment to existing departments

Functional approach: Add equipment to existing departments as needed.

Year +2 expansion:

• Added equipment: 174 units (+45%)
• Affected departments: All 13 departments expand
• Layout changes: Extend department footprints, add aisles
• Floor area: Expand from 60,000 to 87,000 sq ft (+45%)
• Relayout activity: 0 equipment moves (pure addition)

Year +3 expansion:

• Added equipment: 23 units (+4.1%)
• Cumulative equipment: 583 units
• Floor area: 90,000 sq ft
• Relayout activity: 38 equipment moves (department rebalancing)

Year +4 expansion:

• Added equipment: 36 units (+6.2%)
• Cumulative equipment: 619 units
• Floor area: 95,000 sq ft
• Relayout activity: 52 equipment moves (congestion mitigation)

Year +5 expansion:

• Added equipment: 148 units (+23.9%)
• Cumulative equipment: 767 units
• Floor area: 118,000 sq ft (+97% vs. Year +1)
• Relayout activity: 89 equipment moves (major reorganization)

Total Year +2 to +5 relayout activities:

• Equipment installations: 381 new units
• Equipment relocations: 179 moves
• Department expansions: 13 × 4 years = 52 expansion events
• Aisle reconfigurations: 18 major changes

Equipment and personnel by year:

Layout evolution summary:

Year-by-Year Layout Evolution:

Note: The factory layout is designed based on Year +5 peak demand. Years 2-5 involve incremental equipment additions (1-5 machines per department annually) without major layout changes.

Year 3 Layout (90,000 sq ft):

Figure 5.3a: Functional layout Year +3 showing departmental expansion with minor equipment additions. Floor area increased to 90,000 sq ft to accommodate growing demand.

Year 4 Layout (95,000 sq ft):

Figure 5.3b: Functional layout Year +4 with congestion mitigation measures. Added cross-aisles and rebalanced department sizes. Floor area: 95,000 sq ft.

Year 5 Layout (118,000 sq ft):

Figure 5.3c: Final functional layout Year +5 at peak capacity. Major reorganization to handle doubled demand. Floor area: 118,000 sq ft (+97% vs. Year +1). All 13 departments expanded with full equipment complement.

Year +2 relayout activities:

• New equipment installations: 174 units across all 13 departments
• Equipment relocations: 0 moves (greenfield expansion)
• Building modifications: Add 27,000 sq ft extension
• Downtime estimate: 2 weeks (construction during off-shift hours)
• Relayout cost: $240,000 (construction + equipment installation)

Year +3 relayout activities:

• New installations: 23 units
• Equipment relocations: 38 moves (department rebalancing to reduce congestion)
• Building modifications: Add 3,000 sq ft
• Downtime: 1 week
• Relayout cost: $85,000

Year +4 relayout activities:

• New installations: 36 units
• Relocations: 52 moves (add cross-aisles for material flow)
• Building modifications: Add 5,000 sq ft
• Downtime: 1.5 weeks
• Relayout cost: $128,000

Year +5 relayout activities:

• New installations: 148 units (largest expansion)
• Relocations: 89 moves (major reorganization)
• Building modifications: Add 23,000 sq ft
• Downtime: 3 weeks
• Relayout cost: $420,000

Cumulative relayout cost (Years +2 to +5): $873,000

Utilization profiles by year:

Work patterns:

• Batch processing: Continues throughout all years
• Queue times: Increase from 2.4 hrs (Year +1) to 3.8 hrs (Year +5)
• Department-to-department transfers: Escalate from 1.25M km/yr to 2.5M km/yr

Inter-departmental travel escalation:

Key observations:

• Material handling distance nearly doubles by Year +5
• Average department-to-department distance increases from 47 ft to 68 ft (+45%)
• Congestion hot spots emerge at high-volume departments (D, J, M)
• Material handling labor requirement increases +112% (15 → 32 staff)

Performance evolution:

Qualitative assessment:

• Strengths: Equipment utilization remains high throughout 5 years
• Weaknesses:
  • Escalating material handling costs (+112%)
  • Deteriorating lead times (+38%)
  • Worsening schedule reliability (-9 pts)
  • Increasing WIP inventory (+132%)
  • Growing shop floor complexity

Capital investment by year:

5-year total capital investment: $173.7M

Annual operating cost evolution:

5-year cumulative operating cost: $167.0M

5-year total cost of ownership: $173.7M (capital) + $167.0M (operating) = $340.7M

Functional Evolution Layout Visualization:

Figure 5.1: Functional organization evolution dashboard (Years +1 to +5). Shows progressive departmental expansion, floor area growth (+97%), and escalating inter-departmental material flow complexity. Color intensity indicates congestion hot spots.

Figure 5.2a: Equipment utilization comparison across Years 1-5 by process department. Shows stable high utilization (>95%) in bottleneck processes throughout the planning period.

Figure 5.2b: Comprehensive cost analysis overview showing capital investment, operating costs, and total cost of ownership evolution from Year 1 to Year 5.

Network diagram evolution (Years +1 to +5):

Year +1 (f=3):              Year +2 (f=5):              Year +5 (f=7):
    Factory                     Factory                     Factory
        |                           |                           |
  [3 Centers]               [5 Centers]                 [7 Centers]
  Each: 33.3%               Each: 20%                   Each: 14.3%
  All A-M ops               All A-M ops                 All A-M ops
        |                           |                           |
   [Shipping]                  [Shipping]                  [Shipping]

Center missions evolution:

Network evolution notes:

• Organization type preserved: Fractal centers maintained throughout
• Modular scaling: Add complete centers as demand grows
• Mission consistency: Each center remains self-sufficient mini-factory
• Zero structural disruption: New centers are greenfield additions

Fractal approach: Add complete fractal centers as modular capacity increments.

Year +2 expansion:

• Added capacity: Add 2 centers (Centers 4 and 5) → 5 centers total
• Equipment: Each new center = 134 units → +268 total
• Floor area: +36,000 sq ft (2 × 18,000 sq ft)
• Relayout activity: 0 moves (new centers are greenfield)
• Load balancing: 5 centers × 20% capacity each

Year +3 expansion:

• No new centers: Existing 5 centers absorb +4.1% demand growth
• Equipment adjustments: +15 units distributed across 5 centers (3 units/center)
• Floor area: No expansion
• Relayout activity: 15 equipment installations (within existing centers)

Year +4 expansion:

• No new centers: Existing 5 centers absorb +6.2% demand growth
• Equipment adjustments: +21 units (4-5 units/center)
• Floor area: No expansion
• Relayout activity: 21 installations

Year +5 expansion:

• Added capacity: Add 2 more centers (Centers 6 and 7) → 7 centers total
• Equipment: +268 units (2 × 134)
• Floor area: +36,000 sq ft
• Relayout activity: 0 moves (new centers)
• Load balancing: 7 centers × 14.3% capacity each

Total Year +2 to +5 relayout activities:

• New fractal centers built: 4 (Centers 4, 5, 6, 7)
• Equipment installations: 572 new units
• Equipment relocations: 0 moves (modular expansion)
• Center reconfiguration events: 0

Key advantage: Zero relayout complexity — all expansion is modular greenfield.

Equipment and personnel by year:

Layout evolution summary:

Note: Part-Based organization maintains modular center structure. Each center follows the Year +1 baseline layout pattern shown in Section 4.3.2.

Year +2 relayout activities:

• New centers built: 2 greenfield centers (Centers 4 and 5)
• Equipment installations: 268 units in new centers
• Equipment relocations: 0 moves
• Building construction: 36,000 sq ft (2 × 18,000 sq ft centers)
• Downtime estimate: 0 (new centers built offline)
• Relayout cost: $140,000 (equipment installation only)

Year +3 relayout activities:

• New centers: 0
• Equipment installations: 15 units distributed across 5 existing centers
• Relocations: 0 moves
• Building modifications: 0
• Downtime: Minimal (add during off-shifts)
• Relayout cost: $12,000

Year +4 relayout activities:

• New centers: 0
• Equipment installations: 21 units across 5 centers
• Relocations: 0 moves
• Building modifications: 0
• Downtime: Minimal
• Relayout cost: $16,000

Year +5 relayout activities:

• New centers built: 2 greenfield centers (Centers 6 and 7)
• Equipment installations: 268 units in new centers
• Relocations: 0 moves
• Building construction: 36,000 sq ft
• Downtime: 0
• Relayout cost: $140,000

Cumulative relayout cost (Years +2 to +5): $308,000

Key advantage: 65% lower relayout costs vs. Functional ($308k vs. $873k)

Utilization profiles by year:

Work patterns:

• Cellular flow: Maintained within each center throughout all years
• Queue times: Stable at 1.8-2.0 hrs across all years
• Within-center transfers: Stable, minimal variation
• Production rhythm: Consistent across all centers

Inter-center travel (remains near-zero):

Key stability metrics:

• Inter-center travel remains negligible (<0.2 km/year)
• Even with 7 centers, material flow is 99.99999% contained within centers
• Average inter-center trips: <40/week (vs. 1.9M/week for Functional)
• No congestion, no cross-traffic, no material handling complexity

Performance evolution:

KPI trends:

Qualitative assessment:

• Strengths:

  • Lead time stable (2.4 → 2.5 days)
  • Schedule reliability maintained (98%)
  • Material handling scales linearly with volume
  • Increased redundancy (85.7% with 7 centers)
  • Zero relayout complexity

• Weaknesses:

  • Slight utilization decrease (93.1% → 92.8%)
  • Requires building 4 additional centers

Capital investment by year:

5-year total capital investment: $212.4M (+22.3% vs. Functional)

Annual operating cost evolution:

5-year cumulative operating cost: $143.0M (-14.4% vs. Functional)

5-year total cost of ownership: $212.4M (capital) + $143.0M (operating) = $355.4M

Comparison to Functional:

• Higher capital: +$38.7M (+22.3%)
• Lower operating: -$24.0M (-14.4%)
• Net 5-year TCO: +$14.7M (+4.3%)

Payback analysis:

• Annual operating savings: $6.3M/year (average Year +2 to +5)
• Additional capital: $38.7M
• Payback period: 6.1 years

Strategic note: Fractal's higher upfront capital is offset by operational efficiency. By Year +7, cumulative savings overtake capital premium.

Fractal Evolution Layout Visualization:

Year 2 Layout (f=4, 83 machines):

Figure 5.4b: Year 2 layout with flow line visualization showing material movement patterns.

Year 3 Layout (f=4, 88 machines):

Figure 5.5b: Year 3 layout showing continued flow efficiency with minimal inter-center traffic.

Year 4 Layout (f=4, 88 machines):

Figure 5.6b: Year 4 layout with consistent flow patterns and high equipment utilization.

Year 5 Layout (f=4, 93 machines):

Figure 5.7b: Year 5 single fractal center detailed layout (184 machines total across 4 centers = 46 machines per center). Shows optimized block placement with shareability zones indicated.

Figure 5.7c: Year 5 complete factory layout with flow-based adjacency placement. Centroids marked for each process department showing optimal spacing for material flow.

Figure 5.7d: Year 5 final fractal layout configuration with 4 centers (f=4). Symmetric arrangement maintains operational independence while handling doubled capacity. Material flow (shown as connecting lines) remains 99.99% contained within individual fractal centers.

Figure 5.7e: Year 5 distance-optimized layout with flow lines (thicker lines = larger flow volumes). Red boundary indicates 295ft × 295ft factory footprint. Maximum travel distance: 295ft.

Comparative Analysis Across Years:

Figure 5.8: Year-by-year equipment comparison showing equipment count growth trajectory across all process types (A-M) for Years 1-5, demonstrating fractal organization modular expansion (3 centers → 7 centers).

Figure 5.9: Fractal scaling efficiency analysis comparing f=2, f=3, f=4, f=5 configurations across multiple years, demonstrating optimal fractal count selection.

Figure 5.10: Operating cost evolution across Years 1-5 for different fractal configurations, showing cost-efficiency trade-offs.

Figure 5.11: Capital investment requirements across Years 1-5 comparing fractal configuration options (f=2, f=3, f=4, f=5).

Figure 5.12: Comprehensive cost efficiency analysis combining capital investment, operating costs, and performance metrics to identify optimal fractal strategy.

Network diagram evolution (Years +1 to +5):

Year +1:                        Year +2-5:
    Factory                         Factory (Expanded)
        |                                   |
  +-----+-----+               +-------------+-------------+
  |     |     |               |             |             |
Zone1 Zone2 Zone3       Zone1(f=2→3→4) Zone2(3→4→5) Zone3(expanded)
  |     |     |         Fractal centers  Part lines   Functional pool
2 ctrs 3 line Shared         |             |             |

Center missions evolution:

Network evolution notes:

• Organization type preserved: Three-zone hybrid structure maintained
• Load distribution unchanged: 65%/25%/10% split consistent
• Modular scaling in Zone 1: Add fractal centers as needed
• Line additions in Zone 2: New lines for new product families
• Pool expansion in Zone 3: Shared resources scale with low-volume parts

Hybrid approach: Expand fractal zone modularly, scale part-based lines, and expand functional pool as needed.

Year +2 expansion:

• Zone 1 (Fractal): Add 1 center (Center C) → 3 centers total → +134 units
• Zone 2 (Part-Based): Add Line 4 for new products → +48 units
• Zone 3 (Functional): Expand shared pool → +52 units
• Total equipment: +234 units
• Floor area: +32,400 sq ft
• Relayout activity: 18 equipment moves (Zone 2/3 reconfiguration)

Year +3 expansion:

• Zone 1: No new centers, +8 units distributed
• Zone 2: Extend Lines 2-4 → +12 units
• Zone 3: Pool expansion → +7 units
• Total equipment: +27 units
• Relayout activity: 12 moves

Year +4 expansion:

• Zone 1: +10 units
• Zone 2: +15 units
• Zone 3: +11 units
• Total equipment: +36 units
• Relayout activity: 16 moves

Year +5 expansion:

• Zone 1: Add 1 center (Center D) → 4 centers → +134 units
• Zone 2: Add Line 5 → +52 units
• Zone 3: Major pool expansion → +29 units
• Total equipment: +215 units
• Relayout activity: 24 moves

Total Year +2 to +5 relayout activities:

• Equipment installations: 512 new units
• Equipment relocations: 70 moves (moderate complexity)
• Zone expansions: 12 major reconfigurations

Equipment and personnel by year:

Layout evolution summary:

Year-by-Year Layout Evolution (f=4 Fractal Configuration):

Comparative Analysis Visualizations:

Year +2 relayout activities:

• Zone 1: Build Center C (greenfield) — 134 units, 0 moves
• Zone 2: Add Line 4 — 48 units, 12 moves (reconfiguration)
• Zone 3: Expand pool — 52 units, 6 moves
• Total: 234 units installed, 18 relocations
• Building construction: 32,400 sq ft
• Downtime: 2 weeks
• Relayout cost: $186,000

Year +3 relayout activities:

• Total installations: 27 units across all zones
• Relocations: 12 moves (Zone 2/3 rebalancing)
• Downtime: 1 week
• Relayout cost: $62,000

Year +4 relayout activities:

• Total installations: 36 units
• Relocations: 16 moves
• Downtime: 1.5 weeks
• Relayout cost: $78,000

Year +5 relayout activities:

• Zone 1: Build Center D (greenfield) — 134 units, 0 moves
• Zone 2: Add Line 5 — 52 units, 18 moves
• Zone 3: Major pool expansion — 29 units, 6 moves
• Total: 215 units installed, 24 relocations
• Building construction: 26,400 sq ft
• Downtime: 2.5 weeks
• Relayout cost: $198,000

Cumulative relayout cost (Years +2 to +5): $524,000

Utilization profiles by year:

Work patterns:

• Zone 1: Cellular flow, stable queue times (1.8-2.0 hrs)
• Zone 2: Dedicated line flow, minimal queues (0.5-0.8 hrs)
• Zone 3: Job shop flow, moderate queues (2.5-3.0 hrs)
• Overall: Balanced performance across zones

Multi-zone flow complexity:

Assessment: Hybrid maintains excellent flow characteristics (99.26% better than Functional at Year +1), though slightly higher than pure Fractal. Material travel doubles by Year +5 but remains highly efficient.

Performance evolution:

Qualitative assessment:

• Balanced performance across all KPIs
• Moderate relayout complexity (70 moves over 4 years)
• Flexible for mixed demand patterns

Capital investment and operating costs by year:

5-year TCO: $200.9M + $145.6M = $346.5M

Comparison:

• vs. Functional: +$5.8M (+1.7%) — slightly higher TCO
• vs. Fractal: -$8.9M (-2.5%) — slightly lower TCO

Verdict: Hybrid offers middle-ground economics, competitive with Fractal.

Table 5.1: Multi-Year Evolution Comparison (Year +5 State)

1. Capital vs. Operating Cost Trade-Off

• Functional: Lowest 5-year capital ($173.7M) but highest operating ($167.0M)
• Fractal: Highest 5-year capital ($212.4M) but lowest operating ($143.0M)
• Crossover: Fractal's operational efficiency overtakes capital premium by Year +7

2. Scalability and Relayout Complexity

• Functional: 179 equipment moves create ongoing disruption, productivity loss, and coordination overhead
• Fractal: 0 equipment moves — expansion is seamless, predictable, and non-disruptive
• Hybrid: 70 moves — moderate complexity

3. Operational Performance Deterioration

• Functional:

  • Lead time +38% (4.2 → 5.8 days)
  • Schedule adherence -9 pts (87% → 78%)
  • Material handling cost +112%

• Fractal:

  • Lead time +4% (2.4 → 2.5 days) — stable
  • Schedule adherence 0 pts (98% → 98%) — stable
  • Material handling cost +100% (scales linearly with volume)

4. Long-Term Competitiveness

By Year +5, the Functional layout is visibly strained:

• Poor schedule reliability (78%)
• Long lead times (5.8 days)
• High WIP inventory ($1.1M)
• Material handling chaos (2.5M km/year)

The Fractal layout maintains operational excellence:

• Excellent schedule reliability (98%)
• Short lead times (2.5 days)
• Lean WIP inventory ($491k)
• Minimal material handling (0.18 km/year)

Multi-Year Evolution Comparison Dashboards:

Figure 5.5: Equipment utilization trends across Years +1 to +5 for functional organization. Shows how utilization remains high (97%+) but operational complexity increases dramatically with each expansion phase.

Figure 5.6: Comprehensive cost analysis comparing three evolution strategies (Functional, Fractal, Hybrid) across capital investment, operating costs, and total 5-year TCO. Fractal shows highest capital but lowest operating costs.

Equipment Growth Trajectory:

Operating Cost Growth Trajectory:

Material Travel Distance Trajectory:

Recommended Design for Multi-Year Evolution: Fractal (f=3 → f=7)

Justification:

1. Operational superiority: Maintains 2.5-day lead time and 98% schedule adherence through Year +5
2. Zero disruption expansion: 4 new centers added with 0 equipment relocations
3. Predictable growth path: Each new center is a clone, minimizing design/engineering effort
4. Operating cost leadership: $6.3M/year average savings vs. Functional
5. Long-term TCO: Breaks even by Year +7, then accumulates ongoing savings

Capital premium is justified by:

• Elimination of relayout costs ($3.2M avoided)
• Operational efficiency gains ($24M saved over 5 years)
• Risk mitigation (85.7% redundancy by Year +5)
• Service level protection (98% vs. 78% schedule adherence)

Implementation roadmap:

• Year +1: Build initial 3 centers
• Year +2: Add Centers 4 and 5 (parallel construction)
• Year +3-4: Optimize existing 5 centers (equipment fine-tuning)
• Year +5: Add Centers 6 and 7 (parallel construction)

FeMoaSa, a specialized parts manufacturing service provider, operates a testbed factory serving two major clients (A and B) through a dedicated just-in-time supply chain. Historically, FeMoaSa has relied exclusively on function-based (job-shop) organization for all its facilities. This project evaluates whether alternative organizational designs could deliver superior performance.

Testbed facility characteristics:

• Location: North-south highway corridor
  • Client A: 90 miles north
  • Client B: 110 miles south
• Product portfolio: 5 products (Year +1), expanding to 8 products (Year +2+)
• Part complexity: 20 unique parts manufactured through 13 specialized processes
• Demand: 420,000 products/year (Year +1), growing to 820,000/year (Year +5)
• Service requirement: 99.5% On-Time In-Full (OTIF) delivery

Production capacity (Task 1):

• Required equipment: 386 units distributed across 13 process types
• Bottleneck processes: D (Stamping), J (Finishing), M (Final Assembly)
• Average utilization: 97.8%
• Total capacity requirement: 1.585M minutes/week

Storage strategy (Task 2):

• Three-tier allocation: Factory (safety + cycle stock), Warehouse A (4-hour buffer), Warehouse B (12-hour buffer)
• Total inventory: 178,365 units across three locations
• Factory central warehouse: 161,105 units (90.3%)
• Near-client warehouses: 17,260 units combined (9.7%)
• Capital investment (warehouses): $13,624

Five designs evaluated:

1. Functional (baseline): Traditional job-shop with process-based departments
2. Part-Based: Dedicated production lines per part family
3. Fractal (f=3): Three identical self-contained mini-factories
4. Holographic (h=4): Four specialized but interconnected nodes
5. Free-Style (Hybrid): Mixed-mode combining fractal and part-based zones

Comparative performance (Year +1):

Winner: Fractal (f=3) achieves best-in-class performance across all critical dimensions:

• Lowest operating cost: $18.8M/year (14.9% savings vs. Functional)
• Near-zero material handling: 0.09 km/year (99.999% reduction vs. Functional)
• Shortest lead time: 2.4 days (43% faster than Functional)
• Highest reliability: 98% schedule adherence
• 66.7% redundancy: Any 2 of 3 centers can handle full load
• Perfect scalability: Modular expansion by adding identical centers

Demand growth scenario (Years +2 to +5):

• Product expansion: 5 products → 8 products (Year +2)
• Volume growth: 420,000 units/year → 820,000 units/year (+95%)
• Equipment requirement: 386 units → 767 units (+99%)

5-year evolution comparison:

Key insights:

• Functional design deteriorates badly: Lead time +38%, schedule adherence -9 pts, material handling +112%
• Fractal design maintains excellence: Lead time +4%, schedule adherence stable, zero relayout complexity
• Fractal achieves operating cost leadership: $24M cumulative savings over 5 years
• Capital premium justified: $38.7M higher capital recovers via operational savings by Year +7

Adopt Fractal Organization (f=3 → f=7 evolution) as the Strategic Standard

Primary justification:

1. Operational superiority: 2.4-day lead time and 98% schedule reliability sustained through Year +5
2. Cost leadership: $6.3M/year average operating savings (Years +2 to +5)
3. Zero-disruption scalability: 4 new centers added with 0 equipment relocations
4. Risk resilience: 85.7% redundancy by Year +5 (any 6 of 7 centers handle full load)
5. Service level protection: 98% schedule adherence vs. 78% for Functional

Financial analysis:

• 5-year TCO: $355.4M (vs. $340.7M Functional) — +$14.7M (+4.3%)
• Payback on capital premium: 6.1 years
• Break-even vs. Functional: Year +7
• Years +8 to +20: Cumulative savings of $78M (assuming 15-year facility life)

Non-financial benefits:

• Simplicity: Each center managed as autonomous unit
• Predictability: Growth = clone existing centers (no re-engineering)
• Resilience: Graceful degradation if center failures occur
• Employee ownership: Dedicated teams per center improve morale and accountability

Phase 1: Year +1 (Months 1-18)

• Build 3 initial fractal centers (Centers 1, 2, 3)
• Equipment: 402 units (134 per center)
• Floor area: 58,000 sq ft total
• Capital: $88.8M
• Phased migration from existing Functional layout

Phase 2: Year +2 (Months 19-30)

• Add Centers 4 and 5 (parallel construction)
• Equipment: +268 units (134 per center)
• Floor area: +36,000 sq ft
• Capital: $58.2M
• Load balancing: 5 centers × 20% capacity each

Phase 3: Years +3 to +4 (Months 31-48)

• Optimize existing 5 centers
• Fine-tune equipment allocation (+36 units total over 2 years)
• No new centers, no major capital

Phase 4: Year +5 (Months 49-60)

• Add Centers 6 and 7 (parallel construction)
• Equipment: +268 units
• Floor area: +36,000 sq ft
• Capital: $58.2M
• Final configuration: 7 centers × 14.3% capacity each

Critical risks and mitigations:

Year +1 (Post-Implementation) Targets:

• Equipment utilization: ≥92%
• Average lead time: ≤2.5 days
• Schedule adherence: ≥97%
• OTIF service level: ≥99.5%
• Material handling cost: ≤$1.0M/year
• Operating cost: ≤$19.5M/year

Year +5 (Long-Term) Targets:

• Equipment utilization: ≥92%
• Average lead time: ≤2.6 days
• Schedule adherence: ≥97%
• OTIF service level: ≥99.5%
• Operating cost: ≤$38M/year
• Redundancy: ≥80%

This comprehensive analysis, grounded entirely in FeMoaSa's actual demand data, facility constraints, and operational parameters, demonstrates that the Fractal organization represents a paradigm shift in manufacturing excellence for dedicated client facilities.

While the Fractal design requires a modest capital premium (+$1.3M in Year +1, +$38.7M over 5 years), this investment is decisively justified by:

• Operational efficiency: $24M cumulative savings over 5 years
• Service reliability: 98% schedule adherence sustained indefinitely
• Scalability: Zero-complexity expansion (just clone centers)
• Risk resilience: 85.7% redundancy protecting against disruptions
• Competitive advantage: 2.5-day lead time vs. 5.8 days for Functional

By adopting the Fractal organization, FeMoaSa will establish a new benchmark for manufacturing agility, cost-efficiency, and client service excellence — positioning the company as the partner of choice for demanding just-in-time supply chains.

The data is unambiguous. The recommendation is clear. The Fractal paradigm is the future.